Cyclic adaptive receivers for DS-CDMA signals

ABSTRACT

In a receiver receiving a signal, the signal including data which is at least modulated by a cyclic sequence, a method for operating the receiver, the method including the steps of receiving a portion of the signal, the portion being modulated by a predetermined section of the cyclic sequence, receiving an additional portion of the signal, the additional portion being modulated by the predetermined section of the cyclic sequence, jointly processing the portion and the additional portion and producing a set of receiver parameters, the receiver parameters minimizing a predetermined cost function for the predetermined section of the cyclic sequence.

FIELD OF THE INVENTION

The present invention relates to methods and systems for receivingdirect-sequence code division multiple access signals, in general and tomethods and systems for adaptively receiving such signals, inparticular.

BACKGROUND OF THE INVENTION

In recent years, direct-sequence (DS) code division multiple access(CDMA) spread spectrum communication systems and methods experiencegrowing attention worldwide. The IS-95 cellular communication standardis one example for application of DS-CDMA communications, which aredescribed in TIA/EIA/IS-95-A, “Mobile Station-Base Station CompatibilityStandard for Dual-Mode Wideband Spread Spectrum Cellular System,” Feb.27, 1996.

Other implementations of CDMA can be found in third generation cellularsystems, wireless multimedia systems, personal satellite mobile systems,and more. The basic principle of direct sequence code division multipleaccess communications, is that each user is assigned with a distinctspreading code, which is often referred to as a pseudo noise (PN)sequence. The spreading code bits (also called chips), are used tomodulate the user data. The number of chips used to modulate one datasymbol is known as the spreading factor (processing gain) of the system,and it is related to the spreading in bandwidth between the(unmodulated) user data and the CDMA signal.

In its simplest form, the base-band equivalent of the transmitted CDMAsignal is, $\begin{matrix}{{T\lbrack n\rbrack} = {\sum\limits_{i = 1}^{K}{{a_{i}\left\lbrack \left\lfloor {n/{SF}} \right\rfloor \right\rbrack} \cdot {{PN}_{i}\lbrack n\rbrack}}}} & {{Equation}\quad 1}\end{matrix}$

where SF is the spreading factor, └/SF┘ denotes the integer part ofn/SF, a_(i)[└n/SF┘] and PN_(i)[n] are the data symbol and spreading codeof the i-th user, respectively, and K is the number of active users.Note that by the definition of └n/SF┘, a_(i)[└n/SF┘] is fixed for SFconsecutive chips, in accordance with the definition above that eachdata symbol is modulated by SF chips.

If T_(S) and T_(C) denote the symbol and chip intervals in seconds,respectively, then T_(S)=SF·T_(C). The chip rate is defined as 1/T_(C),and the symbol rate is defined as 1/T_(S). Accordingly, the chip rate isSF times greater than the symbol rate.

In a DS-CDMA system, all of the users are continuously transmitting overthe same frequency band. Thus, at the receiver end, each user isdistinguishable from all other users, only through his spreading code.The spreading codes are therefore designed to minimize cross-talkeffects between the different users. Conventional systems often useorthogonal spreading sequences.

In practice, however, channel distortions and asynchronicity modify thetransmitted signals, and as a consequence, cross-talks between the usersexist even when orthogonal spreading codes are utilized by thetransmitter.

A plurality of receiver structures are known in the art for DS-CDMAsignals, including single-user (SU) and multi-user (MU) receivers,interference cancellation (IC) receivers, and more.

A conventional single-user receiver correlates the received signal withthe spreading code of the desired user (user no. 1), as follows$\begin{matrix}{{y_{1}\lbrack m\rbrack} = {\frac{1}{2 \cdot {SF}}{\sum\limits_{n = 1}^{SF}{{R\left\lbrack {{m \cdot {SF}} + n} \right\rbrack} \cdot {{PN}_{1}\left\lbrack {{m \cdot {SF}} + n} \right\rbrack}^{*}}}}} & {{Equation}\quad 2}\end{matrix}$

where R[n] denotes the received signal after down conversion andsampling and “*” denotes the complex conjugation. For simplicity weassume QPSK signaling in Equation 2. A simplistic example is provided,by setting K=2 (i.e. a system which includes two users) and discardingchannel degradation (i.e. R[n]=T[n]). Hence, the following expression isobtained by substituting Equation 1 into Equation 2,

y ₁ [m]=a ₁ [m]+CrossCorr_(1,2) [m]·a ₂ [m]  Equation 3

where $\begin{matrix}{{{CrossCorr}_{i,j}\lbrack m\rbrack} = {\frac{1}{2 \cdot {SF}} \cdot {\sum\limits_{l = 1}^{SF}{{{PN}_{i}\left\lbrack {{m \cdot {SF}} + l} \right\rbrack}^{*} \cdot {{PN}_{j}\left\lbrack {{m \cdot {SF}} + l} \right\rbrack}}}}} & {{Equation}\quad 4}\end{matrix}$

The term CrossCorr_(1,2)[m]·a₂[m] in Equation 3 denotes the interferencecaused to user 1 by user 2. This simple example reveals a well knownweakness of the SU receiver, namely, its performance is governed by thenoise level induced by the cross-talk from all other channel users (seefor example, A. J. Viterbi, “CDMA Principals of Spread SpectrumCommunication”, Addison-Wesley Publishing Company, 1995). A moreadvanced SU receiver includes some means of interference cancellation,which are aimed at reducing these cross-talks, and improving thereceiver's performance. For example, see the following references:

Yoshida, “CDMA-AIC highly spectrum Efficient CDMA cellular system basedon adaptive interference cancellation”, IEICE transactions oncommunication v e79-b n Mar. 3, 1996, p. 353-360,

A. Yoon, “A Spread spectrum multi-access system with co-channelinterference cancellation”, IEEE journal of selected areas incommunications, September 1993,

U.S. Pat. No. 5,105,435 to Stilwell, entitled “Method And Apparatus ForCanceling Spread Spectrum Noise”, and

Y. Li, “Serial interference cancellation method for CDMA” electronicsletters, September 1994.

Multi-user (MU) receivers jointly demodulate several or all of thereceived signals associated with the currently active users. Thestructure of MU receivers is much more complicated than that of SUreceivers, but their performance is significantly better since thesereceivers are less sensitive to cross-talks between the users. (see forexample, S. Verdu “Multi-user Detection” Cambridge University Press,1998, and the references therein).

In practice, the communication link between the transmitter and thereceiver is often time varying. Therefore, the CDMA receiver, which canbe an SU, MU or IC receiver, is required to be adaptive, thereby beingcapable of tracking the time variations of the communication channel.See for example U.S. Pat. No. 5,572,552 to Dent et. al, entitled “Methodand system for demodulation of down-link CDMA signals”. See also, G.Woodward and B. S. Vucetic, “Adaptive Detection for DS-CDMA,”Proceedings of the IEEE, Vol 86, No. 7 July 1998.

Adaptive algorithms, like those available for DS-CDMA applications, aredesigned to minimize the expectation of a predetermined cost function(preferably a convex one) with respect to the receiver's parameters. Forexample, S. Verdu, “Adaptive Multi-User Detection”, Proc. IEEE Int.Symp. On Spread Spectrum Theory and Applications, (Oulu Finland, July1994), is directed to an adaptive least-mean-squares (LMS) MU algorithmwhich minimizes the mean squared error between the transmitted andreconstructed symbols, i.e.

MSE _(i) ≡E{(â ; _(i) [n]−a _(i) [n])²}  Equation 5

where â ;_(i)[n] are the MU receiver output samples at the i-thterminal, and a_(i)[n] are the transmitted symbols of the i-th user. Thecost function in Equation 5 requires training sequences. In other words,the receiver must know the exact value of at least some of thetransmitted symbols (the a_(i)[n]'s) in order to minimize this cost.

Other methods, which are known in the art, do not require training data.S. Verdu, “Adaptive Multi-User Detection”, Proc. IEEE Int. Symp. OnSpread Spectrum Theory and Applications, (Oulu Finland, July 1994), isalso directed to such a method. This method encompasses a decisiondirected approach, which replaces the unknown a_(i)[n]'s by estimationvalues thereof.

In the binary case, for example, a_(i)[n] accepts only two levels: “1”and “−1”. Thus, an estimate of a_(i)[n] can be obtained from the sign ofthe corresponding receiver outputs. In this case, the cost in Equation 5reduces to

E{(â ; _(i) [n]−Sign{â ; _(i) [n]})²}  Equation 6

Another method known in the art is described in M. Honig, U Madhows andS. Verdu, “Blind Adaptive Multi-User Detection, IEEE Trans. onInformation Theory, July 1995. This reference is directed to a methodwhich is based on the fact that under certain conditions, the cost inEquation 5 is equivalent to the following cost

OE _(i) ≡E{â ; _(i) [n] ²}  Equation 7

in the sense that the minimization of these two different cost functionsyields the same receiver.

Since the criterion in Equation 7 does not involve the a_(i)[n]'s, thereis no need for a training sequence. The cost in Equation 7 is known asthe minimum output energy (MOE) cost, since the receiver is updated sothat the energy at its outputs is minimized. The resulting MOE adaptivealgorithms are referred to as “blind” multi-user algorithms, since theyoperate “blindly” without knowing the transmitted bits.

It is often convenient to express the cost function in terms of sampleaveraging instead of stochastic expectations. For example, the MSE costcan be defined, at time instant as follows: $\begin{matrix}{{{MSE}_{i}(n)} \equiv {\sum\limits_{k = 1}^{n}{\left( {{{\hat{a}}_{i}\lbrack k\rbrack} - {a\lbrack k\rbrack}} \right)^{2}\lambda^{({n - k})}}}} & {{Equation}\quad 8}\end{matrix}$

where 0<λ≦1 is an exponential forgetting factor giving more weight torecent samples than to previous ones, thus allowing trackingcapabilities.

The following references are directed to an adaptive recursive leastsquares (RLS) type algorithm for the minimization of this criterion:

H. V. Poor and X. Wang, “Code aided interference suppression for DS/CDMAcommunications: Interference suppression capability”, IEEE Tran. OnComm, September 1997.

H. V. Poor and X. Wang, “Code aided interference suppression for DS/CDMAcommunications: Parallel Blind Adaptive Implementations”, IEEE Tran. OnComm, September 1997.

Similar algorithms can be derived for the cost function in Equation 7,by re-writing it in the following form $\begin{matrix}{{{OE}_{i}(n)} \equiv {\sum\limits_{k = 1}^{n}{{{\hat{a}}_{i}\lbrack k\rbrack}^{2}\lambda^{({n - k})}}}} & {{Equation}\quad 9}\end{matrix}$

Reference is now made to FIG. 1A, which is a schematic illustration of asystem for adaptive detection of a DS-CDMA signal, generally referenced80, which is known in the art. System 80 is basically a processing unit,which implements any of the above methods. The received samplesy[1],y[2], . . . , y[m], are provided as input to the processor. Theprocessor, implementing any of the above methods, calculates theadaptation parameters {circumflex over (θ)}[m] for minimizing the costfunction which characterizes the receiver 80.

It would be obvious to someone skilled in the art, that the receivedsamples y[1],y[2], . . . , y[m] may also be vector valued, e.g. theoutputs from a bank of SU receivers each tuned to a different user.

Reference is now made to FIG. 1B which is a schematic illustration of abank of rake receivers, known in the art. It is noted that a rakereceiver is a single user (SU) receiver.

Section 50 includes an array 52 of rake receivers and a processor 56,connected thereto. The array 52 includes a plurality of rake receivers54A, 54B, 54C and 54M, which are set to receive the signals of as muchas M users.

The input samples to the processor 56 are vector valued in this case, sothat each sample Y[i] is given by${Y\lbrack i\rbrack} = \begin{bmatrix}\begin{matrix}\begin{matrix}{Y\lbrack i\rbrack}_{1} \\{Y\lbrack i\rbrack}_{2}\end{matrix} \\\vdots\end{matrix} \\{Y\lbrack i\rbrack}_{M}\end{bmatrix}$

where Y[i]_(k) is the i-th sample of the k-th rake receiver.

The embodiment in FIG. 1B is often utilized in adaptive MU receiverswhere the processor 56 can detect the transmitted information of user 1by processing the samples provider by rake receiver 54A, while takinginto consideration the influence of the respective samples of the seconduser, as provided by the second rake receiver (54B), the respectivesamples of the third user, as provided by the third rake receiver (54C)and so forth.

Adaptive algorithms are often conveniently described in terms of theirbandwidth. An adaptive algorithm is considered to have an overallresponse of a low-pass filter due to the inherent averaging operationthat is either implicitly or explicitly dominant in any adaptive scheme.The bandwidth of this equivalent low-pass response is considerably lowerthan that of the data, and it governs the tracking and noise rejectioncapabilities of the adaptive algorithm. A large bandwidth implies fasttracking but relatively high residual noise (i.e. large error varianceof {circumflex over (θ)}[m], whereas low bandwidth implies good noiserejection but poor tracking capabilities.

In many DS-CDMA systems, the spreading code is much longer than thesymbol period (the down-link of IS-95 systems, for example). Adaptivealgorithms, like the ones reported in the above references, whosebandwidth is lower than the symbol rate, are inappropriate for suchsystems. This is due to the fact that these algorithms are unable totrack the fast varying interference between the users (whose bandwidthis proportional to the symbol rate since a new interference value isproduced with each new data symbol). The reason for the fast varyingnature of the interference lies in the fact that when the PN sequencespans more than one data symbol, different portions of the PN sequenceare utilized in Equation 4 with different data symbols. Thus, thecross-correlation accepts a different value with each new data symbol.

In some cases, this situation is unavoidable, (e.g. when randomspreading codes are utilized). However, in most cases of practicalinterest, the spreading codes are non-random and finite.

SUMMARY OF THE PRESENT INVENTION

It is an object of the present invention to provide a novel method forreceiving a DS-CDMA signal, which overcomes the disadvantages of theprior art.

It is another object of the present invention to provide a novel DS-CDMAreceiver, which overcomes the disadvantages of the prior art.

In accordance with the present invention there is provided a method forreceiving DS-CDMA signal. The method is for implementing in a receiverreceiving a signal, where the signal includes data which is at leastmodulated by one cyclic sequence. The method includes the steps of:

receiving a portion of the signal, where the portion is modulated by apredetermined section of the cyclic sequence,

receiving an additional portion of the signal, where the additionalportion is modulated by the same predetermined section of the cyclicsequence,

jointly processing the portion and the additional portion, and

producing a set of receiver parameters, which minimize a predeterminedcost function for the predetermined section of the cyclic sequence.

The method of the invention can also include the step of predeterminingsections within the cyclic sequence. It is noted that these sections caninclude one or more elements of the cyclic sequence.

According to another aspect of the invention, the received signal isdemodulated by the cyclic sequence, thereby extracting the data symbolswhich are contained therein. Then, the above operations are performedfor the symbols, with respect to the predetermined sections of thecyclic sequence, where preferably, the length of these sections is inthe order of a symbol.

Accordingly, the method of the present invention with respect to thisaspect, includes the steps of:

demodulating the signal, by the cyclic sequence, thereby producing aplurality of received samples,

determining a plurality of sections, each section having a length of atleast one sample, each the section being demodulated by a predeterminedportion of the cyclic sequence,

detecting portions of the demodulated signal, which are associated witheach of the sections,

jointly processing the detected portions, which are associated by aselected one of the sections, and

producing a set of receiver parameters for each the sections, thereceiver parameters minimizing a predetermined cost function for theselected section.

It is noted that the received signal can be a signal is a DS-CDMA signalor any other spread signal which is modulated by a cyclic sequence.

The demodulating and extracting of the data symbols can include rakedemodulating the DS-CDMA signal, using a rake receiver.

In accordance with another aspect of the invention, there is thusprovided a receiver for detecting a signal, where the signal includesdata which is at least modulated by a cyclic sequence. The receiverincludes a plurality of processing units, each of the processing unitsbeing associated with a predetermined section of the cyclic sequence,and a distributing unit, connected to each of the processing units.

The distributing unit receives the signal, detects portions of thesignal, each of the portions being associated with one of thepredetermined sections. The distributing unit provides selected ones ofthe portions to a selected one of the processing units, wherein both theselected portions and the selected processing unit are associated withthe same predetermined section. Each of the processing units processesthe selected portions, thereby producing set of receiver parameterswhich minimize a predetermined cost function for that specific section.

In accordance with a further aspect of the invention there is thusprovided a receiver for detecting a signal. The signal includes datawhich is at least modulated by a cyclic sequence. The receiver includesa despreading unit, for demodulating the signal by the cyclic sequence,thereby producing a demodulated signal, a plurality of processing units,each the processing units being associated with a predetermined sectionof the cyclic sequence, and a distributing unit, connected between thedespreading unit and each of the processing units.

Accordingly, this receiver demodulates the received signal according tothe cyclic sequence and operates on the demodulated symbols, withrespect to their location, as they were modulated, within the cyclicsequence. It is noted that the despreading unit can include a rakereceiver.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be understood and appreciated more fully fromthe following detailed description taken in conjunction with thedrawings in which:

FIG. 1A is a schematic illustration of an adaptive system for receivinga CDMA signal, which is known in the art;

FIG. 1B is a schematic illustration of an adaptive system for receivinga CDMA signal, including a bank of rake receivers, which is known in theart;

FIG. 2 is an illustration of a cyclic PN spreading signal, used inconventional DS-CDMA systems;

FIG. 3 is a schematic illustration of a receiver, generally referenced100, constructed and operative in accordance with a preferred embodimentof the present invention;

FIG. 4 is a schematic illustration of a receiver, generally referenced150, constructed and operative in accordance with another preferredembodiment of the present invention;

FIG. 5 is a schematic illustration of a method for operating thereceiver of FIG. 3, operative in accordance with a preferred embodimentof the present invention;

FIG. 6 is a schematic illustration of a receiver, generally referenced400, constructed and operative in accordance with another preferredembodiment of the present invention;

FIG. 7 is a schematic illustration of a receiver, generally referenced450, constructed and operative in accordance with a further preferredembodiment of the present invention; and

FIG. 8 is a schematic illustration of a multi-user receiver, constructedand operative in accordance with another preferred embodiment of thepresent invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The present invention overcomes the disadvantages of the prior art, byproviding a novel method which utilizes the cyclic nature of thecross-talk in DS-CDMA systems, in which the span of the PN sequence isgreater than one symbol.

According to the present invention, the cyclic spreading sequence isbroken into a plurality of sections, each including segments atpre-specified locations. Each of these sections is assigned with adifferent cost function and a respective set of receiver parameters,which are used to minimize that cost function. During receptions,portions of the received signal, respective of a section, are jointlyprocessed, so as to dynamically update the respective set of receiverparameters.

Reference is now made to FIG. 2, which is an illustration of a cyclic PNsignal of period L.

The signal is basically constructed of a repetitive predeterminedsequence of samples, wherein sequence 10 is the initial sequence,sequence 20 is a first repetition of the sequence and sequence 30 is asecond repetition of the sequence.

Sequence 10 includes L samples designated 1, 2, 3 . . . L. Sequence 20includes L samples designated L+1, L+2, L+3 . . . 2L. Sequence 30includes L samples designated 2L+1, 2L+2, 2L+3 . . . 3L.

Since all three sequences are identical, then, sample 1 is identical tosample L+1 as well as to sample 2L+1. In general, a sample i in theinitial sequence is identical to a respective sample i+kL, wherein k isthe serial number of the repetitive sequence and L is the length of thecyclic PN sequence.

For example, a conventional receiver attempts to minimize apredetermined cost function, by dynamically adapting a pre-specified setof receiver parameters. These parameters are continuously calculatedfrom the received samples, sample by sample. Accordingly, and withreference to FIGS. 1 and 2, sample 2 is jointly processed with sample 1,sample 3 is jointly processed with samples 2 and 1, sample L+2 isjointly processed with all of the preceding L+1 samples, which includesamples 1, 2, 3, . . . L and L+1.

A receiver according to the present invention, attempts to minimize aplurality of cost functions, each for a predetermined section of thecyclic PN spreading sequence. Accordingly, the receiver dynamicallyadapts pre-specified-sets of receiver parameters, a set per costfunction. The parameters of each set are calculated from portions of thereceived signal which are modulated by a selected section of the cyclicspreading sequence.

According to the method of the present invention, the received samplesare analyzed according to their location within the repetitive PNsequence. A sample, in a selected location within the cyclic sequence,is jointly processed with respective preceding samples, located in thesame location within the cyclic sequence, thereby producing a set ofparameter values for that location.

Accordingly, sample 2L+1 is jointly processed with samples L+1 and 1,thereby yielding a first set of parameter values for the first locationin the cyclic sequence. Sample 2L+2 is jointly processed with samplesL+2 and 2, thereby yielding a second set of parameter values for thesecond location in the cyclic sequence. Sample 3L is jointly processedwith samples 2L and L, thereby yielding the last set of parameter valuesfor the last location in the cyclic sequence. This aspect of the presentinvention is described in detail herein below, in conjunction with FIG.6.

Reference is now made to FIG. 6, which is a schematic illustration of areceiver, generally referenced 400, constructed and operative inaccordance with a preferred embodiment of the present invention.

Receiver 400 includes a plurality of processors 402A, 402B, 402C and402L, and a sample distributing system 410 which includes a plurality ofdown-samplers 406A, 406B, 406C and 406L and a plurality of sample delayunits 404A, 404B, 404C and 404L−1.

The receiver is constructed to have a plurality of branches, whereineach branch includes a down-sampler 406(i) and a respective processor402(i), connected thereto. Accordingly, the first branch includesdown-sampler 406A and processor 402A. The inputs of each adjacentbranches are connected there between via the plurality of delay units404.

The input of the first branch is connected directly to the source of thestream of the received samples, which are sampled at the chip rate. Theinput of the second branch (B) is connected to the input of the firstbranch (A), via a sample delay unit 404A. The input of the third (C)branch is connected to the input of the second branch (B), via a sampledelay unit 404B. The input of the last branch (L) is connected to theinput of the previous branch (L−1, not shown), via a sample delay unit404L.

Each of the sample delay units 404A, 404B, 404C and 404L−1 delays thereceived stream of samples, by one sample time period, before itprovides the stream to the next branch of the receiver 400. Accordingly,the branch, which includes down-sampler 406A and processor 402A,receives the signal as it arrives into the receiver, and the branch,which includes down-sampler 406B and processor 402B, receives thesignal, delayed by one sample time period.

Each of the down-samplers, selects consecutive samples, which arelocated at a predetermined sample distance there between. In the presentexample, all of the down-samples are programmed to provide consecutivesamples, which are L samples apart. Accordingly, down sampler 406Aselects samples 1, L+1, 2L+1 and any other sample which is located atkL+1, where k is any integer and L is the length of the PN sequence.Accordingly, the processor 402A receives these samples, jointlyprocesses them and adaptively provides a set of parameters forminimizing the cost function for this location within the cycliccross-correlation sequence.

Since the stream of samples which is provided to the second branch isdelayed by one sample, then the down sampler 406B selects samples 2,L+2,2L+2, 3L+2 and any other sample which is located at 2+kL. Accordingly,the processor 402B receives these samples, jointly processes them andadaptively provides a set of parameters for minimizing the cost functionfor this location within the cyclic cross-correlation sequence.

It is noted that receiver 400 is adapted to include a branch for eachsample within a cyclic cross-correlation sequence which includes Lsamples.

Each of the processors 402A, 402B, 402C and 402L receives a selectedsample, every L samples, and processes all of the samples it receivedthereby producing a set of parameters for adapting a cost function,which is associated therewith.

According to another aspect of the present invention, samples, which aremodulated by PN elements, which are located in selected locations withinthe cyclic sequence, are grouped together for joint analysis. Forexample, samples in the first location, the third location and the lastlocation in the cyclic sequence are grouped together. Hence, withreference to FIG. 2, samples 2L+1, 2L+3 and 3L are jointly processedwith samples L+1, L+3, 2L, 1, 3 and L, thereby yielding a set ofparameter values corresponding to these selected locations.

Samples, which are located in other locations within the cyclicsequence, can also be grouped, jointly processed, thereby yielding moresets of parameter values.

Reference is now made to FIG. 3, which is a schematic illustration of areceiver, generally referenced 100, constructed and operative inaccordance with preferred embodiment of the present invention.

Receiver 100 includes a plurality of adaptive parameter-set estimationunits 102A, 102B-102K and a distributing unit 104, connected at theinput of each of the adaptive parameter-set estimation units 102.

Each of the adaptive parameter-set estimation units 102, is adapted toprocess a portion of the received signal, and calculated therefrom a setof receiver parameters which minimize a pre specified cost function. Theportion of the received signal includes a group of samples, which aremodulated by elements of the cyclic PN sequence, and located atpredetermined locations, within the cyclic PN sequence.

Adaptive parameter-set estimation unit 102A estimates the parameter set,for the first cost function, according to the first group of samples.With respect to the above example, this group includes samples in thefirst location, the third location and the last location in the cyclicsequence. Hence, with reference to FIG. 2, adaptive parameter-setestimation unit 102A jointly processes samples 2L+1, 2L+3 and 3L withsamples L+1, L+3, 2L, 1, 3 and L, thereby yielding the first set ofparameter values, corresponding to these selected locations.

Similarly, adaptive parameter-set estimation unit 102B estimates thesecond adaptive parameter-set, for the second cost function, accordingto the second group of sample.

Finally, adaptive parameter-set estimation unit 102K estimates the Kthadaptive parameter-set, for the Kth cost function, according to the Kthgroup of samples.

The distributing unit 104 distributes the samples, to each of theadaptive parameter-set estimation units 102, according to theirrespective groups.

Reference is now made to FIG. 5, which is a schematic illustration of amethod for operating each adaptive parameter set estimation unit of thereceiver 100 of FIG. 3, operative in accordance with preferredembodiment of the present invention. The present example provides ademonstration for adaptive parameter set estimating unit 102A, which isprogrammed to process the first group of samples.

In step 200, the adaptive parameter set estimating unit 102A, receivessamples of a signal which are modulated by a repetition of a cyclicspreading PN sequence. With reference to FIGS. 2 and 3, adaptiveparameter set estimating unit 102A receives the first group of samples.

In step 202, the adaptive parameter set estimation unit 102A receivessamples of the signal which are modulated by a later repetition of acyclic sequence. With reference to FIGS. 1 and 2, adaptive parameter setestimating unit 102A receives the first group of samples, of sequence20.

In step 204, the adaptive parameter set estimation unit 102A jointlyprocesses the first group of samples of sequence 10 with the first groupof samples of sequence 20, thereby producing an adaptive receiverparameter set for minimizing the cost function which is associated withthat first group (step 206).

It is noted that the method of the present invention is applied for allof the adaptive parameter set estimation units 102A and 102B-102K, ofthe receiver 100. Each of these units is pre-programmed to addresssamples which are positioned at pre-selected locations within the cyclicspreading sequence. It is noted that each of the groups can include asingle sample.

It will be appreciated that the PN sequence of the communicationstandard IS-95, also known as the down-link short-code, includes 32,768chips. Accordingly, a receiver according to the invention can be adaptedto minimize a different cost function for every chip within the cyclicsequence, or for any group of chips thereof, for example, a group forevery symbol.

According to another aspect of the invention, all processing isperformed in symbol level. The cyclic nature of the PN sequence causesthe symbol level cross-talk between the users to be cyclic. Since the PNsequences are periodical, i.e. PN_(i)[n]=PN_(i)[n+k·L] for any value ofk (and similarly PN_(j)[n]=PN_(j)[n+k·L]) then, we immediately obtainfrom Equation 4 that for all k $\begin{matrix}\begin{matrix}{{{CrossCorr}_{i,j}\lbrack m\rbrack} = \quad {{CrossCorr}_{i,j}\left\lbrack {m + {k \cdot \frac{L}{SF}}} \right\rbrack}} \\{= \quad {{CrossCorr}_{i,j}\left\lbrack {m + {k \cdot N}} \right\rbrack}}\end{matrix} & {{Equation}\quad 10}\end{matrix}$

Wherein i and j denote a first user and a second user, respectively, mdenotes a symbol time index, k denotes an arbitrary integer and N=L/SF.

As can be seen from Equation 10, the cross correlation sequence iscyclic with a period N equal to L/SF symbols. Therefore, the cross-talkterm in Equation 3 is also cyclic with a period of N symbols.

As an example, in the down-link direction of IS-95 systems, L=32768 andSF=64. Thus, according to Equation 10, the cross correlation sequence iscyclic with period N=512 symbols.

The method of the present invention can be implemented for any knowncost function; denoted Cost, as follows. A family of cost functions

Cost[l]0≦l≦N−1  Equation 11

is defined for each of the N different values of the cycliccross-correlation sequence. Accordingly, the present inventionpartitions the data vector,

y ^(l) [m]=y[l+m·N]  Equation 12

where y[m] denotes the data samples (which may also be vector valued)and y^(l)[m] is the data sequence used to minimize the l'th costfunction. Then, a set of N parallel adaptive algorithms are derived suchthat the l'th algorithm utilizes the l'th data sequence to produce thel'th parameter set {circumflex over (θ)}^(l)[m] that minimizes the l'thcost function.

The cost functions used in Equation 11 can correspond to the MSE cost ofEquation 5, its decision directed version presented in Equation 6, theMOE cost of Equation 7, the RLS versions of the MSE and MOE costs ofEquation 8 and Equation 9, respectively, or to some other cost function.In either case, we obtain a generalization of existing adaptivealgorithms to the more general situation of a family of cost functions.Note that existing approaches fall as special cases of the above methodwhen N is set to 1.

Reference is now made to FIG. 7, which is a schematic illustration of areceiver, generally referenced 450, constructed and operative inaccordance with a preferred embodiment of the present invention.

Receiver 450 includes a plurality of processors 452A, 452B, 452C and452N, a despreading unit 458 and a symbol distributing system 410 whichincludes a plurality of down-samplers 456A, 456B, 456C and 456N and aplurality of sample delay units 454A, 454B, 454C and 454N-1.

The receiver is constructed to have a plurality of branches, whereineach branch includes a down-sampler 456(i) and a respective processor452(i), connected thereto. Accordingly, the first branch includesdown-sampler 456A and processor 452A. The inputs of each adjacentbranches are connected there between via the plurality of delay units454.

The input of the first branch is connected directly to the source of thestream of the received symbols, which are sampled at the symbol rate.This is a despreader (reference 458) or a rake receiver, which are knownin the art. The input of the second branch (B) is connected to the inputof the first branch (A), via a sample delay unit 454A. The input of thethird (C) branch is connected to the input of the second branch (B), viaa sample delay unit 454B. The input of the last branch (N) is connectedto the input of the previous branch (N−1, not shown), via a sample delayunit 454N−1.

It should be clear to someone skilled in the art, that the despreadingunit (458) can be composed of several conventional despreaders, eachtuned to a specific user. In this case, the outputs of the despreadingunit (458) are vector valued.

Each of the sample delay units 454A, 454B, 454C and 454N−1 delays thereceived stream of symbols, by one symbol time period, before itprovides the stream to the next branch of the receiver 450. Accordingly,the branch, which includes down-sampler 456A and processor 452A,receives the signal as it arrives into the receiver, and the branch,which includes down-sampler 456B and processor 452B, receives thesignal, delayed by one symbol time period.

Each of the down-samplers, selects consecutive symbols, which arelocated at a predetermined symbol distance there between. In the presentexample, all of the down-samples are programmed to provide consecutivesymbols, which are N symbols apart. Accordingly, down sampler 456Aselects symbols 1, N+1, 2N+1 and any other sample which is located atkN+1, where k is any integer and N is the length of thecross-correlation sequence. Accordingly, the processor 452A receivesthese symbols, jointly processes them and adaptively provides a set ofparameters for minimizing the cost function for this location within thecyclic cross-correlation sequence.

Since the stream of symbols which is provided to the second branch isdelayed by one symbol, then the down sampler 456B selects symbols 2,N+2,2N+2, 3N+2 and any other symbol which is located at 2+kN. Accordingly,the processor 452B receives these symbols, jointly processes them andadaptively provides a set of parameters for minimizing the cost functionfor this location within the cyclic cross-correlation sequence.

It is noted that receiver 450 is adapted to include a branch for eachsymbol within a cyclic cross-correlation sequence which includes Nsymbols.

Each of the processors 452A, 452B, 452C and 452N receives a selectedsymbol, every N symbols, and processes all of the symbols it receivedthereby producing a set of parameters for adapting a cost function,which is associated therewith.

As one example, consider the MSE cost of Equation 5 with the simplesetting of a two-user system. Let the receiver be composed of two PNcorrelators matched to the PN sequences of the two active users,followed by an adaptive processor for jointly combining them. Theoutputs of the two PN correlators are (see Equation 3) given by

 x ₁ [m]=a ₁ [m]+CrossCorr_(1,2) [m]·a ₂ [m]  Equation 13

and similarly,

x ₂ [m]=a ₂ [m]+CrossCorr_(2,1) [m]·a ₁ [m]  Equation 14

We want to adaptively estimate the parameter vector$\hat{\theta} = \begin{bmatrix}\alpha \\\beta\end{bmatrix}$

so that the signal

â ; ₁ [m]=α·x ₁ [m]+β·x ₂ [m]  Equation 15

minimizes MSE₁ as given in Equation 5.

Define the data vector $\begin{matrix}{{Y\lbrack m\rbrack} = \begin{bmatrix}\begin{matrix}\begin{matrix}{{\hat{a}}_{1}\lbrack m\rbrack} \\{a_{1}\lbrack m\rbrack}\end{matrix} \\{x_{1}\lbrack m\rbrack}\end{matrix} \\{x_{2}\lbrack m\rbrack}\end{bmatrix}} & {{Equation}\quad 16}\end{matrix}$

and partition it according to Equation 12, as follows $\begin{matrix}{{{Y^{\prime}\lbrack m\rbrack} \equiv {Y\left\lbrack {l + {m \cdot N}} \right\rbrack}} = {\begin{bmatrix}\begin{matrix}\begin{matrix}{{\hat{a}}_{1}\left\lbrack {l + {m \cdot N}} \right\rbrack} \\{a_{1}\left\lbrack {l + {m \cdot N}} \right\rbrack}\end{matrix} \\{x_{1}\left\lbrack {l + {m \cdot N}} \right\rbrack}\end{matrix} \\{x_{2}\left\lbrack {l + {m \cdot N}} \right\rbrack}\end{bmatrix} \equiv \begin{bmatrix}{{\hat{a}}_{1}^{\prime}\lbrack m\rbrack} \\{a_{1}^{\prime}\lbrack m\rbrack} \\{x_{1}^{\prime}\lbrack m\rbrack} \\{x_{2}^{\prime}\lbrack m\rbrack}\end{bmatrix}}} & {{Equation}\quad 17}\end{matrix}$

According to Equation 11, the l-th data vector—Y^(l)[m] is used toupdate the l-th parameter set—{circumflex over (θ)}^(l) in order tominimize the l-th cost function

 MSE ₁ [l]=E{(â ; ^(l) ₁ [m]−a ^(l) ₁ [m])² }=E{(â ; ₁ [l+m·N]−a ₁[l+m·N])²}  Equation 18

In particular, we write explicitly from Equation 15 that â ;^(l) ₁[m]mis given by

â ; ^(l) ₁ [m]=α ^(l) ·x ^(l) ₁ [m]+β ^(l) ·x ^(l) ₂ [m]=α ^(l) ·x ₁[l+m·N]+β ^(l) ·x ₂ [l+m·N]  Equation 19

The conventional LMS type adaptive algorithm for the minimization ofMSE₁[l] given the data vector Y^(l)[m] is the following $\begin{matrix}\begin{matrix}{{{\hat{\theta}}^{\prime}\lbrack m\rbrack} = \quad {{{\hat{\theta}}^{\prime}\left\lbrack {m - 1} \right\rbrack} - {\mu_{m} \cdot \left( {{{\hat{a}}_{1}^{\prime}\lbrack m\rbrack} - {a_{1}^{\prime}\lbrack m\rbrack}} \right) \cdot \begin{bmatrix}{x_{1}^{\prime}\lbrack m\rbrack} \\{x_{2}^{\prime}\lbrack m\rbrack}\end{bmatrix}}}} \\{= \quad {{{\hat{\theta}}^{\prime}\left\lbrack {m - 1} \right\rbrack} - {\mu_{m} \cdot \left( {{{\hat{a}}_{1}\left\lbrack {l + {m \cdot N}} \right\rbrack} -} \right.}}} \\{{\left. \quad {a_{1}\left\lbrack {l + {m \cdot N}} \right\rbrack} \right) \cdot \begin{bmatrix}{x_{1}\left\lbrack {l + {m \cdot N}} \right\rbrack} \\{x_{2}\left\lbrack {l + {m \cdot N}} \right\rbrack}\end{bmatrix}};{0 \leq l \leq {N - 1}}}\end{matrix} & {{Equation}\quad 20}\end{matrix}$

where μ_(m) is the sequence of step-sizes of the algorithm that areoften set to a constant, i.e. μ_(m)≡μ.

Following the derivation in e.g. S. Haykin, “Adapative Filter Theory”,Prentice-Hall, Second Edition, 1991, it can be shown, that as mapproaches infinity {circumflex over (θ)}^(l)[m] converges to$\begin{matrix}{{\overset{\_}{\theta}}^{\prime} = {\begin{bmatrix}{\overset{\_}{\alpha}\quad}^{\prime} \\{\overset{\_}{\beta}\quad}^{\prime}\end{bmatrix} = \begin{bmatrix}\frac{1}{1 - {{{CrossCorr}_{1,2}\lbrack l\rbrack} \cdot {{CrossCorr}_{2,1}\lbrack l\rbrack}}} \\\frac{- {{CrossCorr}_{1,2}\lbrack l\rbrack}}{1 - {{{CrossCorr}_{1,2}\lbrack l\rbrack} \cdot {{CrossCorr}_{2,1}\lbrack l\rbrack}}}\end{bmatrix}}} & {{Equation}\quad 21}\end{matrix}$

Substituting Equation 13, Equation 14 and Equation 21 into Equation 19,we immediately obtain the desired result $\begin{matrix}{{{\hat{a}}_{1}^{\prime}\lbrack m\rbrack} \equiv {{a_{1}^{\prime}\lbrack m\rbrack}{\forall\left. l\Rightarrow{{{\hat{a}}_{1}\lbrack m\rbrack} \equiv {a_{1}\lbrack m\rbrack}} \right.}}} & {{Equation}\quad 22}\end{matrix}$

that indeed minimizes (zeros) all l cost functions of Equation 18.

With reference to FIG. 7, the element 458 performs the despreadingoperation associated with Equation 13 and Equation 14, the element 410performs the data partitioning of Equation 17, whereas each processor(elements 452) performs the adaptation of Equation 20.

As another example, consider binary signaling and the decision directedcost of Equation 6. Here, the transmitted symbols are not assumed to beknown, and therefore we exclude a₁[m] from the data vector. Thus, we nowdefine the data vector $\begin{matrix}{{Y\lbrack m\rbrack} = \begin{bmatrix}\begin{matrix}{{\hat{a}}_{1}\lbrack m\rbrack} \\{x_{1}\lbrack m\rbrack}\end{matrix} \\{x_{2}\lbrack m\rbrack}\end{bmatrix}} & {{Equation}\quad 23}\end{matrix}$

and partition it according to Equation 12 $\begin{matrix}{{{Y^{\prime}\lbrack m\rbrack} \equiv {Y\left\lbrack {l + {m \cdot N}} \right\rbrack}} = {\begin{bmatrix}\begin{matrix}{{\hat{a}}_{1}\left\lbrack {l + {m \cdot N}} \right\rbrack} \\{x_{1}\left\lbrack {l + {m \cdot N}} \right\rbrack}\end{matrix} \\{x_{2}\left\lbrack {l + {m \cdot N}} \right\rbrack}\end{bmatrix} \equiv \begin{bmatrix}{{\hat{a}}_{1}^{\prime}\lbrack m\rbrack} \\{x_{1}^{\prime}\lbrack m\rbrack} \\{x_{2}^{\prime}\lbrack m\rbrack}\end{bmatrix}}} & {{Equation}\quad 24}\end{matrix}$

The resulting set of LMS type adaptive algorithms are $\begin{matrix}\begin{matrix}{{{\hat{\theta}}^{\prime}\lbrack m\rbrack} = \quad {{{\hat{\theta}}^{\prime}\left\lbrack {m - 1} \right\rbrack} - {\mu_{m} \cdot \left( {{{\hat{a}}_{1}^{\prime}\lbrack m\rbrack} - {{Sign}\left\{ {{\hat{a}}_{1}^{\prime}\lbrack m\rbrack} \right\}}} \right) \cdot \begin{bmatrix}{x_{1}^{\prime}\lbrack m\rbrack} \\{x_{2}^{\prime}\lbrack m\rbrack}\end{bmatrix}}}} \\{= \quad {{{\hat{\theta}}^{\prime}\left\lbrack {m - 1} \right\rbrack} - {\mu_{m} \cdot \left( {{{\hat{a}}_{1}\left\lbrack {l + {m \cdot N}} \right\rbrack} -} \right.}}} \\{\left. \quad {\text{Sign}\left\{ {{\hat{a}}_{1}\left\lbrack {l + {m \cdot N}} \right\rbrack} \right\}} \right) \cdot \begin{bmatrix}{x_{1}\left\lbrack {l + {m \cdot N}} \right\rbrack} \\{x_{2}\left\lbrack {l + {m \cdot N}} \right\rbrack}\end{bmatrix}}\end{matrix} & {{Equation}\quad 25}\end{matrix}$

which are very similar to the adaptive algorithms of Equation 20 onlythat the unavailable sequence a^(l) ₁[m] is replaced by its estimateSign{â ;^(l) ₁[m]}.

As another example, consider the MSE cost of Equation 8 that leads to anRLS adaptive algorithm. Using the data vector in Equation 23 and itspartition in Equation 24, and applying the conventional derivation ofthe RLS algorithm (see e.g. S. Haykin, “Adaptive Filter Theory”,Prentice-Hall, Second Edition, 1991), we obtain $\begin{matrix}\begin{matrix}{{{\hat{\theta}}^{\prime}\lbrack m\rbrack} = \quad {{{\hat{\theta}}^{\prime}\left\lbrack {m - 1} \right\rbrack} - {\left( {{{\hat{a}}_{1}^{\prime}\lbrack m\rbrack} - {a_{1}^{\prime}\lbrack m\rbrack}} \right) \cdot {K^{\prime}\lbrack m\rbrack}}}} \\{{= \quad {{{\hat{\theta}}^{\prime}\left\lbrack {m - 1} \right\rbrack} - {\left( {{{\hat{a}}_{1}\left\lbrack {l + {m \cdot N}} \right\rbrack} - {a_{1}^{\prime}\lbrack m\rbrack}} \right) \cdot {K^{\prime}\lbrack m\rbrack}}}};} \\{\quad {0 \leq l \leq {N - 1}}}\end{matrix} & {{Equation}\quad 26}\end{matrix}$

where $\begin{matrix}{{K^{\prime}\lbrack m\rbrack} = {\frac{1}{\lambda + {\left\lbrack {{x_{1}^{\prime}\lbrack m\rbrack}{x_{2}^{\prime}\lbrack m\rbrack}} \right\rbrack \cdot {P^{\prime}\left\lbrack {m - 1} \right\rbrack} \cdot \begin{bmatrix}{x_{1}^{\prime}\lbrack m\rbrack} \\{x_{2}^{\prime}\lbrack m\rbrack}\end{bmatrix}}} \cdot {P^{\prime}\left\lbrack {m - 1} \right\rbrack} \cdot \begin{bmatrix}{x_{1}^{\prime}\lbrack m\rbrack} \\{x_{2}^{\prime}\lbrack m\rbrack}\end{bmatrix}}} & {{Equation}\quad 27}\end{matrix}$

and $\begin{matrix}{{P^{\prime}\lbrack m\rbrack} = {\frac{1}{\lambda} \cdot \left\lbrack {{P^{\prime}\left\lbrack {m - 1} \right\rbrack} - {{K^{\prime}\lbrack m\rbrack} \cdot \left\lbrack {{x_{1}^{\prime}\lbrack m\rbrack}{x_{2}^{\prime}\lbrack m\rbrack}} \right\rbrack \cdot {P^{\prime}\left\lbrack {m - 1} \right\rbrack}}} \right\rbrack}} & {{Equation}\quad 28}\end{matrix}$

Similar derivations can be performed to the MOE cost of Equation 7 orEquation 9, or any other cost function.

It is hereby noted that when substituting, in either of the algorithmsin Equation 20, Equation 25 or Equation 26, N=1, one obtains a singleadaptive algorithm as in prior art. Since a single-adaptive algorithmwill, at most, converge to a single parameter value {overscore (θ)}, itis clear that Equation 21 cannot be satisfied for all values of l.Therefore, Equation 22 shall not be satisfied. Hence, complete recoveryof input is impossible.

Reference is now made to FIG. 4, which is a schematic illustration of areceiver, generally referenced 150, constructed and operative inaccordance with preferred embodiment of the present invention.

Receiver 150 includes a plurality of the adaptive parameter-setestimation units 152A, 152B-152Q, a distributing unit 154, connected atthe input of each of the adaptive parameter-set estimation units 152 anda despreading unit 156, such as a plurality of rake receivers each tunedto a specific user, connected to the distributing unit.

Each of the adaptive parameter-set estimation units 152, is adapted toprocess a portion of the received signal, including one or more symbols,and calculated therefrom a set of receiver parameters which minimize apre specified cost function. The portion of the received signal includesa group of symbols, which were modulated by elements of the cyclic PNsequence, where these elements are located at predetermined locations,within the cyclic PN sequence.

The distributing unit 154 distributes the samples, to each of theadaptive parameter-set estimation units 152, according to theirrespective groups.

Reference is now made to FIG. 8, which is a schematic illustration of areceiver, generally referenced 300, for multi-user or multi-channelreception, constructed and operative in accordance with anotherpreferred embodiment of the present invention.

Receiver 300 includes a user distributing unit 306 and a plurality ofuser processing units 302A, 302B and 302M. Each of the user processingunits includes a respective group distributing unit 304 and a pluralityof group processing units 310.

The user distributing unit 306 receives the incoming multi-user signaland provides each of the user processing unit 302 with the user signalrespective thereof.

Each of the group distributing units further distributes its receiveduser signal to a plurality of signal portions, each respective of apredetermined section of the cyclic sequence modulated therewith andprovides portion, associated with the same section to a selected groupprocessing unit. Accordingly, each of the group processing units 310produces a set of receiver parameters which are directed at minimizing apredetermined cost function, associated with that particular section.

It will be appreciated by persons skilled in the art that the presentinvention is not limited to what has been particularly shown anddescribed hereinabove. Rather the scope of the present invention isdefined by the claims which follow.

What is claimed is:
 1. In a receiver receiving a signal, the signalincluding data which is at least modulated by a cyclic sequence, amethod for operating the receiver, the method including the steps of:receiving a portion of said signal, said portion being modulated by apredetermined section of said cyclic sequence; receiving an additionalportion of said signal, said additional portion being modulated by saidpredetermined section of said cyclic sequence; jointly processing saidportion and said additional portion; and producing a set of receiverparameters, said receiver parameters minimizing a predetermined costfunction for said predetermined section of said cyclic sequence.
 2. In areceiver receiving a signal, the signal including data which is at leastmodulated by a cyclic sequence, a method for operating the receiver, themethod including the steps of: determining a plurality of sections, eachsaid section including at least one element of said cyclic sequence;detecting portions of said signal, which are modulated by each saidsection, jointly processing detected portions of said signal, which aremodulated by a selected one of said sections; and producing a set ofreceiver parameters for each said sections, said receiver parametersminimizing a predetermined cost function for said selected section. 3.In a receiver receiving a signal, the signal including data symbolswhich are at least modulated by a cyclic sequence, a method foroperating the receiver, the method including the steps of: demodulatingsaid signal, by said cyclic sequence, thereby producing a plurality ofreceived samples; determining a plurality of sections, each said sectionhaving a length of at least one sample, each said section beingdemodulated by a predetermined portion of said cyclic sequence;detecting portions of said demodulated signal, which are associated witheach of said sections; jointly processing said detected portions, whichare associated by a selected one of said sections; and producing a setof receiver parameters for each said sections, said receiver parametersminimizing a predetermined cost function for said selected section. 4.The method according to either of claims 1, 2 and 3, wherein said signalis a DS-CDMA signal.
 5. The method according to claim 3, wherein saidsignal is a DS-CDMA signal and said step of demodulating includes rakedemodulating said DS-CDMA signal.
 6. The method according to claim 5wherein said step of demodulating includes rake demodulating accordingto a plurality of users.
 7. A receiver for detecting a signal, thesignal including data which is at least modulated by a cyclic sequence,the receiver comprising: a plurality of processing units, each saidprocessing units being associated with a predetermined section of saidcyclic sequence, and a distributing unit, connected to each saidprocessing units, wherein said distributing unit receives said signal,detects portions of said signal, each said portions being associatedwith one of said predetermined sections, wherein said distributing unitprovides selected ones of said portions to a selected one of saidprocessing units, wherein both said selected portions and said selectedprocessing unit being associated with a selected one of saidpredetermined sections, and wherein each said processing unitsprocessing said selected portions, thereby producing set of receiverparameters, said receiver parameters minimizing a predetermined costfunction for said selected predetermined section.
 8. The receiveraccording to claim 7, wherein each said sections comprise at least oneelement of said cyclic sequence.
 9. The receiver according to claim 7,wherein each said portions includes at least one element of said data.10. A receiver for detecting a signal, the signal including data whichis at least modulated by a cyclic sequence, the receiver comprising: adespreading unit, for demodulating said signal by said cyclic sequence,thereby producing a demodulated signal, a plurality of processing units,each said processing units being associated with a predetermined sectionof said cyclic sequence, and a distributing unit, connected between saiddespreading unit and each said processing units, wherein saiddistributing unit receives said demodulated signal, detects portions ofsaid demodulated signal, each said portions being associated with one ofsaid predetermined sections, wherein said distributing unit providesselected ones of said portions to a selected one of said processingunits, wherein both said selected portions and said selected processingunit being associated with a selected one of said predeterminedsections, and wherein each said processing units processing saidselected portions, thereby producing set of receiver parameters, saidreceiver parameters minimizing a predetermined cost function for saidselected predetermined section.
 11. The receiver according to claim 10,wherein said despreading unit comprises a plurality of rake receiver.12. The receiver according to claim 10, wherein each said portionsincludes at least one element of said data.
 13. The receiver accordingto either of claims 7 and 10, wherein said signal is a DS-CDMA signal.